Since around 2017, Google has been pushing ahead with its own transformation into the world’s leading AI company. The company also wants to implement this internally.
“It’s important to be honest”, OpenAI founder Sam Altman wrote these days in the context of the question of whether artificial intelligence eliminates more jobs than it creates. The occasion was OpenAI’s new art AI DALL-E 2, which generates graphics from sentences.
Altman estimates that at first the creative work, then the cognitive and finally the physical work can be replaced by artificial intelligence. Google’s finance division is already looking at phase 2.
AI is faster and better than humans
Kristin Reinke is the head of Google’s finance department. In an interview with the Wall Street Journal, she reports on the progress made in automating its own finance operations through machine learning.
“We use [machine learning] in almost every area of finance to modernize our accounting, manage risk, or improve our [operational] processes or working capital,” Reinke says.
Controllers at Google would use machine learning to close the books and identify outliers. As an example, Reinke cites flow analysis, which used to take a day of manual work to compile spreadsheets.
“Now it only takes an hour or two, and the quality of the analysis is better. We can spot trends faster and diagnose outliers,” Reinke says. Google teams use natural language processing among other tools to detect anomalies.
Google wants maximum automation – and needs to build trust to achieve it
Google’s finance team has access to an AI tool for forecasting accuracy. It outperforms human forecasts 80 percent of the time, according to Reinke, but would still be used reluctantly because of a lack of trust in the software.
With feedback from its own analysts, Google plans to further develop the tool with a focus on granularity, transparency and structuring of the models. The goal is to better understand the forecasts and thus be able to trust them more.
“Everything that can be automated, we try to automate,” says Reinke, referring to the routine activities of a financial organization.
Human judgment, however, is still necessary and cannot be automated, he says, just like humans as an interface with people in other companies.
“When you sit down with the business and discuss your problem, you will never be able to automate that. That kind of interaction will never be automated,” Reinke predicts.
For new hires, Reinke emphasizes expertise in technical areas such as SQL programming, using business intelligence and AI tools. In the future employees should no longer spend time on things that can be automated.